Multi-Gene Transcriptional Profiling Core

Shou-Ching S. Jaminet
Center of Vascular Biology Research (CVBR)
Beth Israel Deaconess Medical Center and Harvard Medical School

Multi-Gene Transcriptional Profiling (MGTP) is a real-time PCR based technique, developed in our lab, for quantifying mRNA copy numbers per cell with high reproducibility and accuracy [1-3]. It

  • provides a quantitative view of systemic gene expression (see example in Figure 1), and molecular fingerprinting of different cell types (see example in Figure 2);
  • enables rapid identification of biomarkers for clinical applications (see example in Figure 3); and
  • facilitates exploration of interactions in the gene-protein network [1-3].

The MGTP Core makes these unique and powerful capabilities available to biomedical researchers. Thousands of pre-validated primers are available for use, and others can be designed on demand. Since the MGTP Core’s inception under CVBR in 2009, our work has become part of twenty-two publications [3-27].

Primary Goals of the MGTP Core

  • To make MGTP mRNA quantification services available to investigators, and assist them in advancing their research and obtaining grants;
  • To assist studies of molecular signaling mechanisms and pathways, discovery of biomarkers, and development of clinical assays; and
  • To share the resources of our primer library, which currently includes about 2400 pre-validated human, and mouse genes, through our primer distribution service.

MGTP’s Technical Advantages

  • High specificity and precise quantification . Primer design determines the specificity and accuracy of gene expression measurement in quantitative PCR (qPCR) [28, 29]. All primers used in MGTP are designed within gene-specific regions through rigorous BLASTing and are validated to achieve 99.5% - 99.9% amplification efficiencies.
  • High reproducibility . Reproducibility of qPCR depends on maintaining high RNA integrity, avoiding DNA contamination, and achieving good cDNA preparation [30]. MGTP assesses RNA integrity and cDNA quality, and through a well established calibration protocol, MGTP is able to maintain run-to-run variations within a ± 20% range.
  • High sensitivity. MGTP provides reliable quantification down to expression levels as low as 0. 1 to 0.01 mRNA copies per cell [3].
  • Small sample requirements . MGTP requires only 0.75 ng total RNA for each gene measured; thus, up to 130 genes can be measured with 100 ng total RNA.
  • Per cell quantification . 18S rRNA is the best known normalizer across samples [31, 32] and is uniformly expressed at about a million copies per cell [3]. By normalizing per million copies 18S, our data essentially provides per-cell abundances for every gene. Per-cell quantification enables MGTP data to be compared across experiments and samples.

Through rigorous BLASTing and testing of primer specificity and amplification efficiency, and careful calibration and normalization to obtain per cell expression, MGTP enables rapid acquisition of multi-gene expression quantification and systematic visualization of gene expression (see Figure 1-3 for our examples).

Services Instrument, and Pricing

Text Box: Table 1. MGTP Core Services and Pricing:

We provide four services (Table 1). Real-time PCR services use an ABI 7500 Real-Time PCR System. Prices are at reagent-only cost for CVBR users and at-cost including labor for other BIDMC users. External academic and industrial users should contact us for pricing.

Primer Distribution Service . Customers can purchase primers directly from us. For each purchase, we provide a 500 ul primer solution containing a mix of forward and reverse primers for the gene and the corresponding sequences. A current list of available primers may be found here: Primer Library. Table 1 provides current pricing.

Representative MGTP Profiles

figure 1
Figure 1 illustrates the ease with which MGTP can distinguish one cell type from another, and its ability to elucidate signaling pathways – in this case, intercellular VEGF signaling from TC to EC.
figure 2
Figure 2 illustrates the ability of MGTP profiling to quickly identify the most relevant and abundantly expressed genes from a large family. This enables investigators to focus on the major players from gene families and to identify biomarkers. Per-cell quantification facilitates study of biochemical signaling mechanisms.
figure 3
Figure 3 illustrates the potential of MGTP profiles using well-chosen biomarkers to detect human disease states. Similar multi-gene biomarker panels may prove ideal for diagnosis and prognosis of many diseases.

Research Applications of MGTP

MGTP is ideal for biomarker identification, for plotting dynamic changes of gene expression during disease or experiment progression, and for monitoring molecular mechanisms and pathways through assessment of global gene expression (Figures 1 & 2).

Clinical Applications of MGTP

Pathological states are reflected in gene expression [33, 34], so MGTP profiles can be used to monitor human disease status.

With well-chosen biomarkers, MGTP profiles readily distinguish normal from pathological samples (see example in Figure 3). In some cases, only one or two genes are needed to achieve this, but more genes always improves the quality of state assessment and may help to identify subgroups within established disease populations. Similar multi-gene transcriptional biomarker panels may prove to be ideal for diagnosis and prognosis of many diseases.

Ultimately, by sampling each patient’s specific state at a molecular level, MGTP holds the potential to provide a molecular foundation for personalized medicine.

1. S.C. Shih, G.S. Robinson, P. Perruzzi, A. Calvo, K. Desai, J. Green, I. Ali, L.E. Smith, D. Senger, Molecular Profiling of Angiogenesis Markers, Am J Pathol 161 (2002) 35-41.
2. S.C. Shih, L.E. Smith, Quantitative multi-gene transcriptional profiling using real-time PCR with a master template, Exp Mol Pathol 79 (2005) 14-22.
3. Y. Wada, D. Li, A. Merley, A. Zukauskas, W.C. Aird, H.F. Dvorak, S.C. Shih, A multi-gene transcriptional profiling approach to the discovery of cell signature markers, Cytotechnology (2010).
4. Y. Wada, H. Otu, S. Wu, M.R. Abid, H. Okada, T. Libermann, T. Kodama, S.C. Shih, T. Minami, W.C. Aird, Preconditioning of primary human endothelial cells with inflammatory mediators alters the "set point" of the cell, Faseb J (2005).
5. K. Yano, P.C. Liaw, J.M. Mullington, S.C. Shih, H. Okada, N. Bodyak, P.M. Kang, L. Toltl, B. Belikoff, J. Buras, B.T. Simms, J.P. Mizgerd, P. Carmeliet, S.A. Karumanchi, W.C. Aird, Vascular endothelial growth factor is an important determinant of sepsis morbidity and mortality, J Exp Med 203 (2006) 1447-1458.
6. M.R. Abid, X. Yi, K. Yano, S.C. Shih, W.C. Aird, Vascular endocan is preferentially expressed in tumor endothelium, Microvasc Res 72 (2006) 136-145
7. M.R. Abid, S.C. Shih, H.H. Otu, K.C. Spokes, Y. Okada, D.T. Curiel, T. Minami, W.C. Aird, A novel class of vegf-responsive genes that require forkhead activity for expression, J Biol Chem 281 (2006) 35544-35553
8. J.A. Nagy, D. Feng, E. Vasile, W.H. Wong, S.C. Shih, A.M. Dvorak, H.F. Dvorak, Permeability properties of tumor surrogate blood vessels induced by VEGF-A, Lab Invest 86 (2006) 767-780.
9. Y. Okada, K. Yano, E. Jin, N. Funahashi, M. Kitayama, T. Doi, K. Spokes, D.L. Beeler, S.C. Shih, H. Okada, T.A. Danilov, E. Maynard, T. Minami, P. Oettgen, W.C. Aird, A three-kilobase fragment of the human Robo4 promoter directs cell type-specific expression in endothelium, Circ Res 100 (2007) 1712-1722.
10. M.R. Abid, K.C. Spokes, S.C. Shih, W.C. Aird, NADPH oxidase activity selectively modulates vascular endothelial growth factor signaling pathways, J Biol Chem 282 (2007) 35373-35385.
11. M.R. Abid, R.J. Nadeau, K.C. Spokes, T. Minami, D. Li, S.C. Shih, W.C. Aird, Hepatocyte Growth Factor Inhibits VEGF-Forkhead-Dependent Gene Expression in Endothelial Cells, Arterioscler Thromb Vasc Biol (2008).
12. K. Yano, Y. Okada, G. Beldi, S.C. Shih, N. Bodyak, H. Okada, P.M. Kang, W. Luscinskas, S.C. Robson, P. Carmeliet, S.A. Karumanchi, W.C. Aird, Elevated levels of placental growth factor represent an adaptive host response in sepsis, J Exp Med 205 (2008) 2623-2631.
13. J.A. Nagy, S.C. Shih, W.H. Wong, A.M. Dvorak, H.F. Dvorak, Chapter 3. The adenoviral vector angiogenesis/lymphangiogenesis assay, Methods Enzymol 444 (2008) 43-64.
14. A.C. Dudley, Z.A. Khan, S.C. Shih, S.Y. Kang, B.M. Zwaans, J. Bischoff, M. Klagsbrun, Calcification of multipotent prostate tumor endothelium, Cancer Cell 14 (2008) 201-211.
15. A.C. Dudley, S.C. Shih, A.R. Cliffe, K. Hida, M. Klagsbrun, Attenuated p53 activation in tumour-associated stromal cells accompanies decreased sensitivity to etoposide and vincristine, Br J Cancer 99 (2008) 118-125.
16. N.I. Shapiro, K. Yano, M. Sorasaki, C. Fischer, S.C. Shih, W.C. Aird, Skin Biopsies Demonstrate Site-Specific Endothelial Activation in Mouse Models of Sepsis, J Vasc Res 46 (2009) 495-502.
17. E. Jin, J. Liu, J.I. Suehiro, L. Yuan, Y. Okada, V. Nikolova-Krstevski, K. Yano, L. Janes, D. Beeler, K.C. Spokes, D. Li, E. Regan, S.C. Shih, P. Oettgen, T. Minami, W.C. Aird, Differential roles for ETS, CREB and EGR binding sites in mediating VEGF receptor 1 expression in vivo, Blood (2009).
18. J. Liu, Y. Kanki, Y. Okada, E. Jin, K. Yano, S.C. Shih, T. Minami, W.C. Aird, A +220 GATA motif mediates basal but not endotoxin-repressible expression of the von Willebrand factor promoter in Hprt-targeted transgenic mice, J Thromb Haemost 7 (2009) 1384-1392.
19. E. Jin, J. Liu, J. Suehiro, L. Yuan, Y. Okada, V. Nikolova-Krstevski, K. Yano, L. Janes, D. Beeler, K.C. Spokes, D. Li, E. Regan, S.C. Shih, P. Oettgen, T. Minami, W.C. Aird, Differential roles for ETS, CREB, and EGR binding sites in mediating VEGF receptor 1 expression in vivo, Blood 114 (2009) 5557-5566.
20. S.C. Shih, A. Zukauskas, D. Li, G. Liu, L.H. Ang, J.A. Nagy, L.F. Brown, H.F. Dvorak, The L6 protein TM4SF1 is critical for endothelial cell function and tumor angiogenesis, Cancer Res 69 (2009) 3272-3277.
21. S.H. Chang, K. Kanasaki, V. Gocheva, G. Blum, J. Harper, M.A. Moses, S.C. Shih, J.A. Nagy, J. Joyce, M. Bogyo, R. Kalluri, H.F. Dvorak, VEGF-A induces angiogenesis by perturbing the cathepsin-cysteine protease inhibitor balance in venules, causing basement membrane degradation and mother vessel formation, Cancer Res 69 (2009) 4537-4544.
22. J.A. Nagy, S.H. Chang, S.C. Shih, A.M. Dvorak, H.F. Dvorak, Heterogeneity of the Tumor Vasculature, Semin Thromb Hemost 36 (2010) 321-331.
23. N.I. Shapiro, E.V. Khankin, M. Van Meurs, S.C. Shih, S. Lu, M. Yano, P.R. Castro, E. Maratos-Flier, S.M. Parikh, S.A. Karumanchi, K. Yano, Leptin exacerbates sepsis-mediated morbidity and mortality, J Immunol 185 (2010) 517-524.
24. A.C. Dudley, T. Udagawa, J.M. Melero-Martin, S.C. Shih, A. Curatolo, M.A. Moses, M. Klagsbrun, Bone marrow is a reservoir for pro-angiogenic myelomonocytic cells but not endothelial cells in spontaneous tumors, Blood (2010).
25. N.S. Ismail, E.A. Pravda, D. Li, S.C. Shih, S.M. Dallabrida, Angiopoietin-1 reduces H(2)O(2)-induced increases in reactive oxygen species and oxidative damage to skin cells, J Invest Dermatol 130 (2010) 1307-1317.
26. K. Matsuda, N. Ohga, Y. Hida, C. Muraki, K. Tsuchiya, T. Kurosu, T. Akino, S.C. Shih, Y. Totsuka, M. Klagsbrun, M. Shindoh, K. Hida, Isolated tumor endothelial cells maintain specific character during long-term culture, Biochem Biophys Res Commun 394 (2010) 947-954.
27. C. Nucera, P. Muzzi, C. Tiveron, A. Farsetti, F. La Regina, B. Foglio, S.C. Shih, F. Moretti, L. Della Pietra, F. Mancini, A. Sacchi, F. Trimarchi, A. Vercelli, A. Pontecorvi, Maternal thyroid hormones are transcriptionally active during embryo-foetal development: results from a novel transgenic mouse model, J Cell Mol Med 14 (2010) 2417-2435.
28. M. Kubista, J.M. Andrade, M. Bengtsson, A. Forootan, J. Jonak, K. Lind, R. Sindelka, R. Sjoback, B. Sjogreen, L. Strombom, A. Stahlberg, N. Zoric, The real-time polymerase chain reaction, Mol Aspects Med 27 (2006) 95-125.
29. S.A. Bustin, R. Mueller, Real-time reverse transcription PCR (qRT-PCR) and its potential use in clinical diagnosis, Clin Sci (Lond) 109 (2005) 365-379.
30. J. Vermeulen, F. Pattyn, K. De Preter, L. Vercruysse, S. Derveaux, P. Mestdagh, S. Lefever, J. Hellemans, F. Speleman, J. Vandesompele, External oligonucleotide standards enable cross laboratory comparison and exchange of real-time quantitative PCR data, Nucleic Acids Res 37 (2009) e138.
31. A. Bas, G. Forsberg, S. Hammarstrom, M.L. Hammarstrom, Utility of the housekeeping genes 18S rRNA, beta-actin and glyceraldehyde-3-phosphate-dehydrogenase for normalization in real-time quantitative reverse transcriptase-polymerase chain reaction analysis of gene expression in human T lymphocytes, Scand J Immunol 59 (2004) 566-573.
32. J.L. Aerts, M.I. Gonzales, S.L. Topalian, Selection of appropriate control genes to assess expression of tumor antigens using real-time RT-PCR, Biotechniques 36 (2004) 84-86.
33. S.B. Fox, Quantitative angiogenesis in breast cancer, Methods Mol Med 120 (2006) 161-187.
34. J. Vermeulen, K. De Preter, A. Naranjo, L. Vercruysse, N. Van Roy, J. Hellemans, K. Swerts, S. Bravo, P. Scaruffi, G.P. Tonini, B. De Bernardi, R. Noguera, M. Piqueras, A. Canete, V. Castel, I. Janoueix-Lerosey, O. Delattre, G. Schleiermacher, J. Michon, V. Combaret, M. Fischer, A. Oberthuer, P.F. Ambros, K. Beiske, J. Benard, B. Marques, H. Rubie, J. Kohler, U. Potschger, R. Ladenstein, M.D. Hogarty, P. McGrady, W.B. London, G. Laureys, F. Speleman, J. Vandesompele, Predicting outcomes for children with neuroblastoma using a multigene-expression signature: a retrospective SIOPEN/COG/GPOH study, Lancet Oncol 10 (2009) 663-671.